American Diplomacy rarely publishes statistical analysis. But because this paper analyzes the debate over foreign aid’s purposes and motivations, we thought it might be of interest to our readers. The paper examines the most recent data on the recipients of official development assistance, presenting fourteen traditional hypotheses regarding foreign aid, and it challenges each one with socio-economic statistics from the CIA’s World Factbook online. The paper concludes that national interest motivations appear to be a major factor in foreign aid, contrary to the many publicly stated explanations. –Ed.

Introduction Foreign aid has always been a hotly debated topic in the post-WWII period. With the inception of the Marshall Plan, the United States carried out one of the greatest economic recovery acts in history. As the Cold War intensified, the idea of the Marshall Plan was expanded to include the rest of the developing world, many of whom were just getting out of the throes of European colonialism and were ripe targets for both superpowers. The Organization for Economic Cooperation and Development (OECD) was created in 1960 by twenty of the wealthiest countries to promote better economic relations between countries. The Development Assistance Committee (DAC) was established thereafter within the OECD to support economic development in the Third World. All donor aid that was at least 25% in grants was considered to be Official Development Assistance (ODA), as opposed to loans and other conditional assistance by institutions like the World Bank. The intentions were ideal but the argument remained as to whether ODA was primarily for economic and humanitarian reasons or did national interests and power politics influence it in reality?

The scholarly debate continues to this day as to what the primary purpose of foreign aid is and should be. Should foreign assistance be for humanitarian purposes and the promotion of socio-economic development in the impoverished world? Or, should it be for national interests and the promotion of security and power? Hans Morgenthau complained more than half a century ago that he, as a realist, could not fully answer this question, given the mixed picture of interests and stated intentions. With current OECD/DAC data along with the CIA’s online WorldFactbook, we may now be able to get a better understanding of what the primary purpose(s) of foreign aid is. This research paper analyzes and evaluates ODA and its recipients by looking at key socio-economic data to determine what may be driving ODA’s direction and purpose.

A Brief Scholarly ReviewProponents of foreign aid as a humanitarian goal have been large and vocal throughout the past half century. Scholarly wise, David Lumsdaine was the leading proponent of the humanitarian strand in foreign assistance. In Moral Vision in International Politics (1993), Lumsdaine argues that modern foreign assistance must have had a strong moral fiber since it reached hundreds of billions of dollars and was supported by large numbers of constituents inside and outside of government for decades. He notes like others (Wood 1986, and Ruttan 1996, et al.) that nothing in all of history compares with the massive wealth transfer between countries (peacefully and voluntarily) and the declared ideal humanitarianism.

Since the end of the Cold War, many scholars (Meernik, Krueger, and Poe 1998, Lancaster 2007, and Heckelman and Knack 2008, et al.) have recognized that foreign assistance has taken on a much greater emphasis for promoting economic development, as well as democracy. The stated goals and ideals of foreign aid are widely promoted, but they often may not reflect reality. The humanitarian research stresses the amount, types, and recipients of foreign aid. The OECD and others have collected much data showing the totals, distribution, and categories of foreign aid. Few studies, however, have attempted to measure the real-world results and effectiveness of foreign aid over the decades, as well as the statistical relationship between ODA and specific humanitarian/national interests; general totals and targets yes, but not the outcomes and socio-economic details. After trillions of ODA over the last several decades, it behooves us to assess the current recipients’ key socio-economic data and, then, determine whether or not humanitarian or national interests have prevailed.

Baldwin (1966 and 1985), Hook (1995), and Lancaster (2007), et al. have argued that foreign aid can be driven by national interests, including security and power politics. David Baldwin’s Economic Statecraft (1985) and Steven Hook’s National Interest and Foreign Aid (1995) are the classic examples showing foreign aid being used primarily for power interests. They state that throughout the Cold War it could be demonstrated that a number of major powers directed their amount and type of foreign assistance to countries that corresponded with their national interests—i.e. security, political, economic, and ideological interests. They point out that foreign aid was often framed domestically as promoting the national interests and that the direction and character of foreign assistance supported these claims. They stress that regardless of the secondary purposes and results that the humanitarianism may have garnered, the underlying purpose of foreign aid was and still is hardcore national interests. They support their claims by looking at the total amount and specific recipients of foreign aid in the post-WWII period and they conclude that the national security and economic interests of the donors must have played a significant and overriding role in which they gave their foreign aid.

Research Data and MethodsIn order to assess whether primarily humanitarian or national interests influence ODA, this paper will use the most recent data (2008) from OECD/DAC. The DAC reports include a list of all of the recipients of ODA, and each recipient’s total GNI (gross national income, which is similar to gross domestic product), GNI per capita, and total population. It should be noted that of the 151 recipients of ODA, not all recipients were sovereign countries and that some GNI and GNI per capita data were not in the DAC reports, so current GDP and GDP per capita were used. All this and remaining data and variables were collected from the CIA’s World Factbook online.

The main research question is whether primarily humanitarian or national interests determine ODA. The dependent variable is Net ODA. The independent variables are socio-economic and military factors that may influence the total amount of ODA and each one in itself proffers a hypothesis. The fourteen independent variables are GNI (GDP) per capita, population size, GNI (GDP) total, literacy, education, life expectancy, infant mortality, and urbanization levels, HIV/AIDS rate, total arable land, total exports, total imports, external debt amount, and military expenditures as a percentage of GDP. Each of these variables is defined and given by DAC and the CIA (see Appendix for specific descriptions). This research project carries out OLS regression analysis to determine the strength of the relationship between Net ODA and each of the independent variables. It, then, uses multiple regression to test a number of grouped variables and all the variables together to determine if the overall analysis and results change according to the different combinations, let alone have any significance between each other (F significance value).

Hypotheses and FindingsThe fourteen hypotheses and independent variables that are tested with Net ODA produced some interesting results. The following data and brief interpretations are based upon OLS regression results using STATA 10.0. More complete tests/findings can be found in the Appendix. A brief summary is as follows.

Hypothesis #1: The lower the GNI per capita of an ODA recipient, the greater the total ODA. This is a significant variable, with a 95% Confidence Interval (numbers do not intersect zero), a T value of 2.93 (greater than 1.96), and a P value of 0.004 (less than 0.05). Its Adjusted R-squared does not appear to be significant in itself, with a 0.0482 figure explaining the DV variance in the model, which means that other relevant variables are needed to see the bigger picture. But, like the rest of the independent variables that we will see below which all have individual Adjusted R-squared numbers that appear insignificant, taken together in multiple regression at the end, the Adjusted R-squared becomes 0.1717 (R-squared is 0.2925), which is significant given this particular study, especially when it comes to national interest factors which stated publicly are not supposed to enter the equation at all. In other words, even small figures for the individual variables and as a whole could end up being significant if they are not specifically related to humanitarian reasons.

Hypothesis #2: The larger the impoverished recipient’s population is, the greater the ODA. This is a significant variable, with a 95% Confidence Interval, a T value of 2.49, and a P value of 0.014. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0335 figure explaining the DV variance in the model, which means that other relevant variables are needed to get a better picture.

Hypothesis #3: The less the recipient’s GNI, the higher the ODA. This is not a significant variable, with no 95% Confidence Interval, a T value of 1.38, and a P value of 0.168. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0061 figure explaining the DV variance in the model, which means that other relevant variables are necessary for a fuller view.

Hypothesis #4: The lower the literacy level of a recipient, the more ODA is given. This is a significant variable, with a 95% Confidence Interval, a T value of 3.28, and a P value of 0.001. Its Adjusted R-squared does not appear to be significant in itself (though higher than the previous ones), with a 0.0635 figure explaining the DV variance in the model, which means that other relevant variables are needed.

Hypothesis #5: The lower the education level of a recipient, the greater the ODA. This is a significant variable, with a 95% Confidence Interval, a T value of 2.36, and a P value of 0.020. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0360 figure explaining the DV variance in the model, which means that other relevant variables would help give us a better picture.

Hypothesis #6: The lower the life expectancy in a recipient, the more ODA. This is a significant variable, with a 95% Confidence Interval, a T value of 2.72, and a P value of 0.007. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0418 figure explaining the DV variance in the model, which means that other relevant variables are needed to complete the view.

Hypothesis #7: The higher the infant mortality rate in a recipient, the greater the ODA. This is a significant variable—in fact, the most significant of all the variables—with a 95% Confidence Interval, a T value of 3.65, and a P value of 0.000. Its Adjusted R-squared does not appear to be significant in itself (but, it is higher than all others), with a 0.0774 figure explaining the DV variance in the model, which means that other relevant variables are needed to see the bigger picture but this one moves us closer.

Hypothesis #8: The lower the urbanization level of a recipient, the higher the ODA. This is not a significant variable, with no 95% Confidence Interval, a T value of 1.14, and a P value of 0.254. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0021 figure explaining the DV variance in the model, which means that other relevant variables are needed to get a fuller picture.

Hypothesis #9: The higher the HIV/AIDS rate in a recipient, the greater the ODA. This is not a significant variable—in fact, it is the least significant variable of them all—with no 95% Confidence Interval, a T value of 0.12, and a P value of 0.908. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0084 figure explaining the DV variance in the model, which means that other relevant variables are very much needed to see the bigger picture.

Hypothesis #10: The less arable land a recipient has, the more ODA is given. This is not a significant variable, with no 95% Confidence Interval, a T value of 1.34, and a P value of 0.183. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0053 figure explaining the DV variance in the model, which means that other relevant variables are necessary.

Hypothesis #11: The more the recipient exports, the higher the ODA. This is not a significant variable, with no 95% Confidence Interval, a T value of 1.28, and a P value of 0.201. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0043 figure explaining the DV variance in the model, which means that other relevant variables are needed to get a better view.

Hypothesis #12: The more the recipient imports, the higher the ODA. This is not a significant variable, with no 95% Confidence Interval, a T value of 1.67 (though, close), and a P value of 0.097. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0118 figure explaining the DV variance in the model, which means that other relevant variables would be helpful in getting a wider picture (see the end for multiple regression using exports and imports variables to find a significant relationship in both these variables when together with Net ODA).

Hypothesis #13: The larger the external debt of the recipient, the more ODA it receives. This is a significant variable, with a 95% Confidence Interval, a T value of 2.41, and a P value of 0.017. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0328 figure explaining the DV variance in the model, which means that other relevant variables are needed, though this is more of a national interest variable (as opposed to a humanitarian variable) so this small percentage of 3.28% may be significant in capturing variance in some cases.

Hypothesis #14: The higher the recipient’s percentage of military expenditures per GDP, the more ODA it receives. This is a significant variable, with a 95% Confidence Interval, a T value of 2.26, and a P value of 0.025. Its Adjusted R-squared does not appear to be significant in itself, with a 0.0324 figure explaining the DV variance in the model, which means that other relevant variables are necessary to get a better picture, though once again this is clearly a national interest variable so this small percentage of 3.24% may be significant in capturing the variance in a number of cases.

Final Analysis: Exports and Imports Regression Model and All the Independent Variables in Multiple Regression. The Exports and Imports independent variables together are significant, with a 95% Confidence Interval for both of them, a T value of -2.54 (exports) and 2.76 (imports), and a P value of 0.012 (exports) and 0.007 (imports). The Adjusted R-squared does not appear to be significant in itself, with a 0.0466 figure explaining the DV variance in the model, which means that other relevant variables are needed to see the bigger picture. Yet, the two variables together produce significant results compared to when the variables were analyzed separately.

All fourteen independent variables together in a multiple regression model produce surprising changes in the overall results of the simple regression models. Six IVs (GNI per capita, population, literacy, education, life expectancy, and external debt) go from significant to not significant (though the external debt variable was very close to being significant). The six IVs that were not significant in the simple regression models (GNI, urbanization, HIV/AIDS, arable land, exports, and imports) remained insignificant in the multiple regression model. The infant mortality and military expenditures variables were the only two variables that were significant in both the simple and multiple regression models. What is notable is that the percentage of military expenditures per GDP variable overtakes the infant mortality variable as the predominant IV in this multiple regression model. The military expenditures variable is significant, with a 95% Confidence Interval, a T value of 2.99, and a P value of 0.004, which is an improvement over the simple regression model, while the infant mortality variable is reduced but is still significant. The overall model’s Adjusted R-squared is 0.1717 (R-squared is 0.2925), which is a significant increase over the simple regression figures, meaning that the fourteen independent variables together explain approximately 17% (29% with R-squared) of the DV variance in the model, which is reasonably significant when considering the overall picture and the fact that just two of the fourteen variables were significant in themselves.

Conclusion Overall, these research findings provide an interesting set of results. On the one hand, in the simple regression models, they support the argument that humanitarian interests play a significant role in foreign aid, with 6 of the 8 significant independent variables being at least nominally for humanitarian reasons (GNI per capita, population, literacy, education, life expectancy, and infant mortality). On the other hand, national interests do play a significant role in the simple regression models of foreign aid, with the recipient’s external debt and military expenditures being significant. Furthermore, after testing multiple variables for further analysis, it was found that exports and imports together play a significant role in influencing net ODA, and military expenditures is the most significant variable in the multiple regression model. This would support the national interest perspective that major donors/powers will be influenced security wise by the natural resources/products a developing country exports along with its willingness to import from the donor country, especially when it comes to what is called tied aid (i.e., a donor’s foreign aid is conditioned on the recipient using the funds to purchase goods and services from the donor itself). With exports and imports together, it means that 10 of the 14 independent variables in the simple regression models are significant, with nearly half (4) being in the category of national interests.

In the end, the military expenditures variable was a surprise from an ODA perspective, in both the simple and multiple regression models. It is common for bilateral military assistance to be given, but for ODA to appear to be influenced heavily by military expenditures is a significant finding. The regression analysis on exports and imports with net ODA was another interesting finding that warrants further investigation, especially in terms of energy security. Finally, the external debt variable is an important finding. Donors may be sending ODA to recipients in order to have them pay back their debts to donors and donor financial institutions, let alone prevent the recipients from defaulting on donor loans. This significant relationship between ODA and the recipient’s external debt demands further research. If ODA is being determined significantly by a recipient’s debt to foreign creditors, then it may be the donor’s special financial interests and powers that are determining a large part of ODA, especially the amount and recipient. This would contradict the whole humanitarian grounds of foreign aid and suggest that donor financial interests and institutions are playing a very influential role in the disbursement of ODA, which would be another significant finding. Moreover, the dismal and most recent socio-economic statistics of many recipients after decades of foreign aid indicate that there may be other non-humanitarian factors involved. All in all, these findings suggest that national interests play a significant role in ODA and that there are ripe opportunities for further research in the foreign assistance area.

Definitions/Measurements of Variables Using the OECD/DAC and the CIA World Factbook

All currency in U.S. Dollars

Net ODA=In Millions. Total Official Development Assistance to a Recipient in 2008 (most recent available information). GNI Per Capita=Gross National Income Per Individual in Exact Currency (GNI/Population). Population=In Millions. GNI=Total In Millions. Literacy=Percent of Population 15 Years and Over Who Can Read and Write. Education=Average Number of Years of Primary and Tertiary Education for Citizens. Life Expectancy=Average Life Span In Years for Citizens. Infant Mortality Rate=Total Deaths Per 1,000 Live Births Urbanization=Percentage of Population Living in an Urban Area HIV/AIDS=Adult Prevalence Rate, i.e. Percentage of Adult Population with HIV/AIDS. Arable Land=Percentage of Arable Land Within a Recipient’s Borders. Exports=Total Exports in Millions. Imports=Total Imports in Millions. External Debt=Total Debt in Millions Owed to Foreign Creditors. Military Expenditures=Percentage of Military Expenditures to GD

Infant Mortality as the Most Significant Independent Variable

Multiple Regressions

Steve Dobransky is an Adjunct Professor at Cleveland State University, and he is completing his Ph.D. at Kent State University. He majors in International Relations. He has an M.A. from Ohio University and a B.A. from Cleveland State University. Contact: sdobrans @ kent.edu.